-
Notifications
You must be signed in to change notification settings - Fork 0
/
make_normflow_dataset.py
131 lines (101 loc) · 3.98 KB
/
make_normflow_dataset.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
import os
import pickle
import json
from argparse import ArgumentParser
import numpy as np
from scipy.stats import qmc
import scipy.stats
from hyperion.constants import Constants
from hyperion.medium import medium_collections
from hyperion.pmt.pmt import make_calc_wl_acceptance_weight
from hyperion.utils import (
calc_tres,
cherenkov_ang_dist,
cherenkov_ang_dist_int,
)
def make_dataset(files, seed, config, tt=4, tts=1.45):
pprop_conf = config["photon_propagation"]
pmt_conf = config["pmt"]
ref_ix_f, _, _, abs_l_f = medium_collections[pprop_conf["medium"]]
path_to_wl_file = os.path.join(
os.path.dirname(__file__), f"data/{pmt_conf['qe_curve']}"
)
wl_acc = make_calc_wl_acceptance_weight(path_to_wl_file)
def c_medium_f(wl):
"""Speed of light in medium for wl (nm)."""
return Constants.BaseConstants.c_vac / ref_ix_f(wl)
rstate = np.random.RandomState(seed)
all_times = []
all_dists = []
all_angls = []
all_nphotons_frac = []
for file in files:
data = pickle.load(open(file, "rb"))
sampler = qmc.Sobol(d=1, scramble=True, seed=rstate)
# npick = min(dlim, len(dataset[0]["times_det"]))
# ixs = rstate.choice(np.arange(len(dataset[0]["times_det"])), size=npick, replace=False)
sim_data = data[0]
det_dist = sim_data["dist"]
isec_times = sim_data["times_det"]
ph_thetas = sim_data["emission_angles"]
# stepss = sim_data["photon_steps"]
# isec_poss = sim_data["positions_det"]
nphotons_sim = sim_data["nphotons_sim"]
wavelengths = sim_data["wavelengths"]
prop_dist = isec_times * c_medium_f(wavelengths) / 1e9
abs_weight = np.exp(-prop_dist / abs_l_f(wavelengths))
wl_weight = wl_acc(wavelengths, pmt_conf["max_qe"])
tres = calc_tres(
isec_times, pprop_conf["module_radius"], det_dist, c_medium_f(700) / 1e9
)
costhetas = 2 * sampler.random_base2(m=6) - 1
obs_angs = np.arccos(costhetas)
for obs_ang in obs_angs:
c_weight = (
cherenkov_ang_dist(
np.cos(ph_thetas - obs_ang), n_ph=ref_ix_f(wavelengths)
)
/ cherenkov_ang_dist_int(ref_ix_f(wavelengths), -1, 1)
* 2
)
tot_weight = abs_weight * c_weight * wl_weight
sum_w = tot_weight.sum()
ixs = np.arange(len(tres))
surv_ph = rstate.choice(
ixs, p=tot_weight / sum_w, size=rstate.poisson(sum_w)
)
times = tres[surv_ph]
if tts > 0:
a = tt**2 / tts**2
b = tts**2 / tt
pdf = scipy.stats.gamma(a, scale=b)
dt = pdf.rvs(size=len(surv_ph), random_state=rstate) - tt
times += dt
all_times.append(times)
all_dists.append(np.ones_like(surv_ph) * det_dist)
all_angls.append(np.ones_like(surv_ph) * obs_ang)
all_nphotons_frac.append([det_dist, obs_ang, sum_w / nphotons_sim])
return (
np.vstack(
[
np.concatenate(all_times),
# np.concatenate(all_weights),
np.concatenate(all_dists),
np.concatenate(all_angls),
]
),
all_nphotons_frac,
)
def main():
parser = ArgumentParser()
parser.add_argument("-i", "--infile", dest="infile", required=True)
parser.add_argument("-o", "--outfile", dest="outfile", required=True)
parser.add_argument("--tts", dest="tts", default=0, type=float)
parser.add_argument("-s", "--seed", dest="seed", default=0, type=int)
parser.add_argument("-c", "--config", type=str, required=True, dest="config")
args = parser.parse_args()
config = json.load(open(args.config))
data = make_dataset([args.infile], config=config, seed=args.seed, tts=args.tts)
pickle.dump(data, open(args.outfile, "wb"))
if __name__ == "__main__":
main()